1. Field of the Invention
The present invention relates generally to systems and methods for identifying liquidity. In particular, the present application relates to systems and methods for determining the presence of hidden limit orders in an order book.
2. Description of the Related Art
There is a demand among financial traders for more transparency and currency of market information in order driven electronic markets, such as the new level 2 and real-time data products offered by NASDAQ and NYSE. Markets which provide electronic limit order books, including, for example, Euronext, London Stock Exchange, XETRA, Spanish Stock Exchange, and Toronto Stock Exchange, provide a measure of currency and transparency. An electronic limit order market is a trading platform where anonymous buyers and sellers post price-quantity pairs—i.e., the quoted bid (or ask) prices and associated quantities (depths) of a stock that the market participant is willing to buy (or sell). Limit order books offer market participants the ability to observe levels of market liquidity by displaying prices and quantities of unexecuted limit orders. Utilizing this data, market participants can implement a range of “game theoretical” strategies and choose limit orders with specified price, quantity, and timing, thus allowing them to minimize execution costs and uncertainty, hide market information, and possibly move the market towards the desired price.
Given concerns associated with information leakage due to order placements, some market venues allow market participants to enter “hidden” limit orders which do not reveal the full share volume size and/or the associated price level (also known as “iceberg”, “undisclosed”, or “discretionary” limit orders). This brings with it a complex interrelationship between exposure risk (adverse selection), market liquidity, and the need for transparency. From a market design point of view, hidden limit orders represent a trade-off between liquidity and transparency. Trading systems need to attract liquidity and trading activity. The availability of hidden limit orders encourages limit order traders, who are otherwise hesitant to fully disclose their trading interests, to supply liquidity—thus increasing the liquidity on the system. However, hidden limit orders volume, by its nature, does not add information to the market and thus, does not help in the market's transparency.
In particular, hidden orders inside the spread will not attract activity to a venue, since most order routing systems can only operate on visible (i.e., displayed) information. Thus, as reported by ANANTH MADHAVAN, “Market microstructure: a survey”, Journal of Financial Markets, 3 (2000), pp. 205-258, hidden limit orders clearly diminish supposed benefits of transparent order driven markets: price efficiency, low costs of market monitoring and less information asymmetries.
The concept of hiding transaction fingerprints has been around for several years, but has recently seen increased popularity due to the advent of algorithmic trading systems such as ITG's “Dark Server” or CSFB's “Guerilla,” which utilize continuous mid-point crosses from “Dark Books.” For illiquid stocks, which have larger intra-day volatility, the concept of hiding allows the market participant to transact with minimum market impact.
Hidden limit orders have become an important limit order type. As disclosed in Hasbrouck and Saar [2002], hidden orders account for more than 12% of all orders executed on Island, and Tuttle [2002] reports that hidden liquidity represents 20% of the inside depth in the Nasdaq 100 stocks. D'Hondt, De Winne, and Francois-Heude [2004] disclose that hidden depth on Euronext Paris accounts for 45% of the total depth available at the best five quotes and 55% of the total depth at the best limits.
These findings suggest that there are underlying factors that cause a market participant to use a hidden versus a visible limit order, considering the controversial rationale behind using hidden limit orders. Consistent with previous literature, there are two main beliefs for the existence of hidden limit orders. First, hidden limit orders can be used by large liquidity traders to reduce their exposure risk by hiding their intent to trade. In other words, liquidity traders use hidden limit orders as a self-protective strategy against other more informed traders. Second, hidden limit orders can be mostly submitted by informed traders to conceal their insider information. By placing (aggressive) hidden limit orders, market participants with insider information can trade quickly and almost unobserved. Therefore, informed traders may prefer using undisclosed versus displayed limit orders for certain market conditions.
Taking into account undisclosed limit orders can dramatically change the picture of the limit order book at any given time of the day. For example, referring to FIG. 1, it can be easily concluded that if instantaneous execution of a buy market order for 1,000 shares of company Argonaut Group Inc. is desired, the cost associated with that trade (benchmarked on the existing mid-quote) would be $0.05 per share. This cost is computed by first assuming that only the observable volume is available and then climbing up the book to pay the following average execution price x:
                    x        =                ⁢                                            500              ×              35.05                        +                          300              ×              35.07                        +                                          (                                  1000                  -                                      (                                          500                      +                      300                                        )                                                  )                            ×              35.12                                1000                                                  =                    ⁢          35.06537                ,            giving a cost per share y of:
                    y        =                ⁢                  35.07          -                      mid            ⁢                                                  ⁢            quote                                                  =                ⁢                  35.07          -          35.02                                        =                ⁢                  0.05          .                    However, if the order book could be reconstructed in a way that included the inferred hidden shares using information from prevailing market conditions, one would then see that the “true” cost for the 1,000 shares is actually only about $0.045 per share:
                    x        =                ⁢                                                                                                  3                    ×                    35                                    +                                      2                    ×                    35.01                                    +                                      5                    ×                    35.02                                    +                                      6                    ×                    35.03                                    +                                                                                                                          543                    ×                    35.05                                    +                                      300                    ×                    35.07                                    +                                      141                    ×                    35.12                                                                                1000                                        =                ⁢                  35.06537          .                    Thus, the cost per share y after hidden volume is considered is:
                    y        =                ⁢                  35.06537          -                      mid            ⁢                                                  ⁢            quote                                                  =                ⁢                  35.06537          -          35.02                                        =                ⁢                  0.04537          .                    
A trader seeing the “true” limit order book instead of FIG. 1 might be willing to consider the opportunity cost relative to the market dynamics associated with removing only a portion of the desired volume from within the spread—which leads to improvement in per share transaction cost. As reported by Pascual and Veredas [2004], the explanatory power of the book is concentrated within the dynamics associated with the visible best quotes. This trader would also be able to evaluate the probability that an order is filled within or below the existing visible best ask price.
Thus, there remains a need for a system that can estimate hidden limit orders and provide a probabilistic “reconstructed” order book including inferred hidden limit orders that allow the trader to factor this information into a trading position.